Abstract / Summary
Dashboards and situation reports publish deaths per confirmed case by health zone, and decisions on investigation and resources lean on these numbers. We ask when such a reported ratio is ready to be read as fatality, and propose a readiness test that combines Bayesian partial pooling, an ascertainment tipping-point analysis and repeated refitting at successive data cut-offs, framed by a surveillance-maturity logic. We applied it to public cumulative confirmed cases and deaths from the 2026 Bundibugyo virus disease epidemic in the Democratic Republic of the Congo (8,728 cases and 4,205 deaths on 6 October). Among 41 health zones with at least 10 cases, North Kivu zones had higher adjusted odds of a reported death than Ituri zones (odds ratio 1.46 (0.99-2.14), 95% credible interval), a point estimate that was insensitive to the prior, the case threshold and omission of single zones. The estimate was nevertheless unstable across cut-offs: the odds ratio was 1.78, 2.54, 1.67, 1.51 and 1.46 on 20 July, 30 August, 30 September, 5 October and 6 October (the last three from identical zones), and its lower credible limit, 1.01 on 5 October, was 0.99 on 6 October. A 1.25-fold difference in the ratio of death to case ascertainment between the provinces would suffice to produce the contrast. Cases and deaths were often reported on the same date, and mapped facility indicators added nothing. By this test, zone-level ratios are not yet interpretable as fatality, although they correctly show where to investigate. In simulated stable epidemics the cumulative decline since 25 August was never matched, whereas the drift ratio over three cut-offs was a weak signal. Surveillance-design actions follow: date and label ratios, show uncertainty and drift, record event dates, audit ascertainment, and link patient records.